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The Intelligent Robot Simulator is developed, a lightweight declarative simulator implemented as a Python library to support navigation learning and benchmarking through rapid construction of reusable and diverse scenarios and efficient execution on accessible platforms.
The Structured Subtask Chain (SSC), a state-transition representation that bridges these extremes, is proposed and instantiate the pipeline on BEHAVIOR-1K for logic verification and content completion.
This work proposes DreamAvoid, a critical-phase test-time dreaming framework that enables VLA models to anticipate and avoid failures, and introduces an autonomous boundary learning paradigm to refine the system's understanding of the subtle boundary between success and failure.
Shape-Aware Reinforcement Learned Model Predictive Control is proposed, a method for safe, efficient, and adaptive navigation in crowds with heterogeneous shapes without geometry simplification that preserves the safety structure and generalizability of MPC while integrating the adaptability and intelligence of RL.
StreamPI is proposed, a streaming multimodal temporal modeling framework that equips single-frame VLA with temporal reasoning capability without introducing any additional parameters and seamlessly inherits pretrained single-frame weights and supports flexible single-frame and multi-frame inference.
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